DDDPlus™. . . The industry’s only in vitro dissolution software for formulation and analytical scientists.
What is DDDPlus?
Reimagine the in vitro dissolution experiment…
Today, dissolution studies are the most frequently used tools in the development, characterization, and utilization process of pharmaceutical dosage forms, both immediate and controlled-release. As a formulation or CMC scientist, you’re often tasked with designing a product which achieves a target in vitro dissolution rate that will, hopefully, lead to desired in vivo exposure levels. Or, the lifecycle management team wants to assess the possibility of creating a once-a-day formulation which is bioequivalent to a B.I.D. (twice a day) or T.I.D. (three times a day) dosing regimen. You don’t have a lot of time or material available, so you begin manufacturing small lots of products to test under different conditions, hoping something works. What if there was a more efficient strategy?
Welcome to DDDPlus™, the industry’s leading mechanistic in vitro dissolution software for formulation and analytical scientists. With DDDPlus, you can model and simulate the in vitro dissolution of active pharmaceutical ingredients (API) and formulation excipients under various experimental conditions in seconds, and begin making informed decisions to help improve your chances for success.
What are we providing with DDDPlus?
We have always been dedicated to carefully implement the best theories and develop novel approaches within the DDDPlus dissolution models. You enter limited physicochemical & manufacturing data, set up your dissolution method, and DDDPlus provides the rest:
Simple, intuitive user interface
Model optimization
High-quality plots & figures for reporting purposes
Excellent customer support
Integration with our other tools, like ADMET Predictor® (QSAR) and GastroPlus® (PBPK & PBBM)
The in vitro processes which are considered in the DDDPlus simulations, and the methods we’ve introduced to parameterize them, are too numerous to list here – instead, take a peek at the DDDPlus brochure for more details.
How is DDDPlus being applied?
The DDDPlus dissolution modeling platform has been utilized by companies across various industries and departments since 2006. Some of the routine applications include:
Assessing formulation strategies to achieve target dissolution profiles
Assisting with dissolution method development
Applying virtual ‘lot-to-lot’ variability effects to help establish dissolution specifications – remove the guesswork associated with the identification of dissolution variability and its impact on PK exposure
Facilitating Quality by Design (QbD) implementation to guide product development
Integrating with GastroPlus™ absorption, PBPK and PBBM models to optimize formulations and generate mechanistic in vitro-in vivo correlations (IVIVCs) – better extrapolation of dissolution inputs for PBPK & PBBM modeling
… and more!
DDDPlus models
DDDPlus models the following dosage forms:
• Long Acting Injectable models for PLGA microspheres
• IR: Solution Model (precipitation options)
• CR: Coated Bead Model
• Powders
• Capsules
• Tablets
• Polymer Matrix (Swellable & Non-Swellable)•
Coated beads
• Bilayer tablets
• Delayed release coated tablets
4 USP experimental apparatus are defined, with estimates of fluid velocity and hydrodynamic effects for each:
• Artificial Stomach Duodenum (ASD)
• Membrane Dissolution
• Biphasic Dissolution
• USP Paddle
• USP Basket
• USP Flow Thru
(closed and open loop options)
• Rotating Disk
• Pion μDISS Profiler™
DDDPlus allows you to select from one of 5 mathematical models to describe the dissolution of any ingredients included in the formulation. The mathematical models for the in vitro dissolution simulation account for the effects of:
• Manufacturing properties for the product (e.g., compression force, tensile strength, mean disintegration times)
• Physicochemical properties of the formulation ingredients under study: pKa’s, aqueous solubility vs. pH, biorelevant solubility, diffusion coefficient, logP, and density
• Particle size distributions for each of the formulation ingredients
• Interactions between the active ingredient and formulation excipients (e.g., solubilizers, disintegrants, wetting agents)
• Microclimate pH-dependence of solubility and dissolution/precipitation
• Basic hydrodynamic effects, including different flow patterns and fluid velocities, for each experimental apparatus
• Micelle-facilitated dissolution through the incorporation of surfactants in the media
• … and more!
Simulation Modes
Single Simulation: based on compound properties (whether measured or predicted through the ADMET Predictor® Module), formulation information, and in vitro dissolution setup, easily run a simulation to predict the time course changes in amount (or percent) dissolved for any ingredient in the product. Also track changes in microclimate and bulk pH levels vs. time.
Parameter Sensitivity Analysis (PSA): select any formulation or experimental parameters to assess the impact of changes on the in vitro dissolution vs. time profiles
3D PSA – now analyze the impact of changes in a ‘design space’ by simulating all combinations of any two selected parameters. Quickly identify an optimal combination that achieves the desired dissolution result
Virtual Trials: run a series of simulations for different dissolution experiments, each of which is described by a random sample of formulation and/or experimental parameters, to imitate the variances expected with actual formulation or experimental setups. This powerful capability allows you to assess the combined effects of variations in formulation or experimental variables on the in vitro dissolution profiles, helping to establish dissolution specifications as you scale up manufacturing. And, when coupled with GastroPlus models, you can begin to translate the dissolution ‘variability’ to expected changes in pharmacokinetic profiles and assess virtual bioequivalence between formulation lots.
Optimization Module:calibrate your DDDPlus dissolution model using experimental in vitro dissolution vs. time data. Fit any combination of parameters to build your baseline model – once built and validated with existing data, use it to explore changes in formulation, experiment and more.
Difference Factor ‘f1’ and Similarity Factor ‘f2’:
The Difference Factor ‘f1’ and Similarity Factor ‘f2’ are recommended for dissolution profile comparisons in the FDA guidance for the industry. Once you run a simulation, you can load a reference profile and use the Difference Factor and Similarity Factor tools in DDDPlus to automatically run simulations across all formulation records and calculate the ‘f1’ and ‘f2’ values.
Model Inputs
Physicochemical Parameters
With DDDPlus, you can add as many excipients to the formulation as you like. This is done through the Formulation Composition window shown. ADMET Predictor Module: using the industry’s #1-ranked Quantitative Structure-Activity Relationship (QSAR) models from our ADMET Predictor program, import chemical structures (as SMILES strings or .mol/.sdf formats) to predict the physicochemical properties required for the DDDPlus. This can provide a quick, reliable foundation for your modeling activities.
DDDPlus now allows you to enter multiple ingredients of the same type: define multiple excipients in the formulation.
Any excipients can be entered, or you can use the database of commonly used excipients, provided with the program, where all necessary physicochemical properties are defined!
You can either input mean radius and standard deviation for the particle size distribution or load your own sieve distribution data (fractional or cumulative).
DDDPlus comes with a tool to easily convert your cumulative particle size distribution data (e.g., D(10), D(50), D(90)) into a full normal or log-normal distribution function.
With DDDPlus, you can enter multiple pKa ionization constants for each ingredient. This information is used to define the aqueous solubility vs. pH profile and in the calculation of media pH during the simulation. Plus, the theoretical logD vs. pH profile is generated, which can be applied to estimate the bile salt solubilization effect
Experimental Setup
Dissolution Method Conditions and Multi-Phase Experiments
With DDDPlus, you can define your dissolution method conditions like apparatus, instrument speed, medium volume and medium type. DDDPlus calculates the fluid velocity automatically based on the instrument speed and apparatus type and utilizes this information to capture basic hydrodynamic effects on the dissolution rate.
You can add as many experimental phases as you want to better mimic the in vivo environment. This can be helpful when trying to design an in vitro dissolution method to achieve a meaningful in vitro-in vivo correlation (IVIVC).
Dissolution Media and Microclimate pH
DDDPlus has a sophisticated pH engine to calculate the dissolution media pH and solubility of each ingredient at the surface and bulk pHs. You can select from more than 90 built-in buffers, including all USP and biorelevant recipes, or easily design your own. You can also vary the concentrations of the different ingredients to create custom buffers at various pH.
Microclimate pH
DDDPlus dynamically calculates the microclimate pH (pH at the diffusion layer of the particle) for each ingredient in the formulation. You can select either “microclimate pH” to calculate the solubility of the ingredient at the diffusion layer or “bulk pH” for solubility in the dissolution media. The “bulk pH” is utilized to capture any potential precipitation effects once the dissolved material reaches the bulk environment.
Surfactants
DDDPlus allows you to add up to 2 surfactants per dissolution media. You have the option to choose from a list of common surfactants or create your own.
Surfactant solubility tool: easily calculate the CMC and/or surfactant enhancement factor provided you have selected a media with one or two surfactants. This applies to non-biorelevant surfactants like SDS, CTAB, BRIJ, CHAPS, etc.
DDDPlus™ ADMET Predictor® Module
The ADMET Predictor Module extends the capability of DDDPlus by enabling you to obtain predictions from structure of all physicochemical parameters required for DDDPlus, providing you with in silico inputs to use for your simulations of in vitro dissolution of pharmaceutical dosage forms. The module uses the same models as our best-in-class ADMET Predictor software.
What Can We Predict?
Updated! Enhanced pKa model developed in collaboration with Bayer HealthCare – ALL models retrained with greater accuracy!
This module automatically generates predictions for the following properties:
Diffusion coefficient in water
The ADMET Predictor Module provides several critical benefits:
by loading a library of chemical structures for your desired excipients, create a database with predicted values for hard to measure properties
use the in silico predictions and Parameter Sensitivity Analysis to guide your in vitro dissolution experiments
begin evaluating different formulation strategies to assess the importance of factors like particle size, solubility and dose on in vitro dissolution
【案例揭秘】 使用DDDplus软件模拟体外溶出行为
来源：【研如玉公众号】
摘 要 ：溶出度测试是许多剂型（包括片剂和胶囊）的性能测试。
本研究的目的是评估计算机模拟是否可以预测两种模型药物的体外溶出度，而这两种不同药物的溶出数据可以获得。使用公布的孟鲁司特钠和格列本脲溶出数据用于模拟。评估了不同的药典和生物溶出度检测介质，体积和转速。另外，使用这些缓冲液评估一个pH值变化方案。DDDPlus™3，Beta版（Simulation Plus，Inc。）用于模拟体外溶出数据。将模拟数据与体外数据进行比较。使用预测数据和观察数据之间的回归系数来评估模拟。孟鲁司特钠统计分析显示，除一种缓冲液外，所有案例的体外释放数据与预测数据之间存在显著相关性。使用单一pH条件，格列本脲在实验数据与预测数据之间存在显著相关性。使用动态pH方案，对于一种生物溶出度介质是显著相关性。模拟显示体外药物释放都对溶解度效应敏感，这证实了它们的BCS归属 II类。使用DDDPlusTM的体外释放的计算机模拟具有在药物开发过程的早期阶段估计体内溶出的潜力。这可能用于选择最合适的溶出条件，以建立IVIVC并开发生物溶出度相关的体外性能测试，以通过设计空间捕获质量控制过程中的关键产品属性。
结 论
DDDPlus™能够预测不同实验条件下孟鲁司特钠和格列本脲的体外释放类型。使用DDDPlus TM的体外释放类型的计算机模拟具有在药物开发过程的早期阶段估计体外溶出行为的潜力。这可以用于选择最合适的溶出条件，一方面建立IVIVC，另一方面开发生物相关的体外性能测试，捕获质量源于设计方法中的关键产品属性或适当的质量控制程序。
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请点击链接：https://mp.weixin.qq.com/s/lRXoboHrvjeYfnCV0sbQ7A
【大力推荐】使用体外和计算机模拟模型进行超出FDA现行标准的崩解试验的依据
来源：【研如玉公众号】
摘 要
药品性能测试是质量源于设计(QbD)方法的重要组成部分，但这个过程往往缺乏对所涉及崩解和溶解过程之间复杂相互作用的潜在机制的理解。尽管美国食品和药品管理局（FDA）最近的一份草案指南允许使用崩解试验替代溶出试验，但是这一标准并未在全球范围内被接受。本研究为使用崩解试验替代溶出试验作为某些速释制剂（IR）的质量控制方法提供了科学依据。提出了一种超出FDA现行标准的机械方法。使用USP浆法在不同的转速下对甲硝唑的速释和缓释制剂进行溶出试验。通过DDSolver进行溶出曲线拟合并通过DDDPlus进行溶出曲线预测。结果表明，Fickian扩散和药物颗粒属性（DPP）可用于解释IR片剂的溶出行为，并且这些制剂因素（例如圆锥形）仅在较低旋转速度下影响溶出行为。对于缓释片剂，DPP不重要时，溶出行为完全受处方控制。为证明当溶出度受DPP控制时，崩解是最重要的制剂属性，在常规和崩解影响介质（DIM）中进行崩解、固有溶出度和溶出测试。片剂崩解受到DIM的影响，并且符合Korsmeyer-Peppas方程的模型，其结果显示制剂在DIM中的溶出有所改善。DDDPlus能够预测片剂溶解在常规介质和DIM中的固有溶出曲线。该研究表明，崩解发生在DPP依赖的溶解之前。该研究表明，在FDA标准之外，崩解也可用作快速崩解片剂的性能测试。科学标准和理由是溶出必须是DPP依赖性的，源于API特征和制剂因素必须可忽略不计。
结 论
在药物开发过程中QbD方法的目标是充分描述和控制所有关键过程和质量属性。如今，基于对产品和工艺性能的全面了解，统计方法和计算机模拟可用于实现合理的产品设计并评估可能的工艺或产品风险。
本研究显示了DDDPlus目前在不同介质和转速下预测API和片剂溶出行为的能力和局限性。通过DDSolver的溶出模型拟合结果能够区分DPP和处方影响的片剂溶出行为。
该研究系统的研究了IR片剂的崩解和溶出行为。发现崩解和溶解可以是依次进行或同时进行的，或者依次进行或同时进行同时发生。如果首先发生崩解，则可能产生DPP主导的溶出过程，并且崩解可以用作速崩片的性能测试（超出FDA现行标准），其科学数据是，溶解与API的特性息息相关，同时依赖于DPP，溶出过程中的处方因素必须可以忽略不计。但是，如果处方显着影响溶出行为，则应将溶出用作质量控制方法。
这种方法将使全球运营的制药公司能够通过崩解试验科学地保证其产品质量规格，而不必非要去迎合可能互相矛盾的各国单独定制的监管指导文件。
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请点击链接：https://mp.weixin.qq.com/s/AlTwCRkqIUIFSLwME9o7mw
【案例揭秘】 使用silico模拟溶出曲线开发多沙唑嗪缓释片
来源：【研如玉公众号】
摘 要
对制剂人员来讲，开发具有适当适当释放特性的缓释（ER）制剂有一定的难度。本研究的目的是通过使用计算模拟技术绘制具有统计学意义的释放曲线来实验设计多沙唑嗪缓释片的开发。试验制备多沙唑嗪缓释片，释放曲线测试条件：USP装置2；900ml不含酶的模拟胃液为介质；37±0.5℃和75rpm测试960min。试验结果填入模拟软件DDDPlus以优化校准常数。通过Design Expert软件调节乳糖和HPMC K100M的处方比例，制备7个不同的处方，使用DDDPlus模拟释放曲线。经过统计分析后，确认一个优化的多沙唑嗪缓释片处方，制备、检测，并与预测的曲线进行比较。两曲线的相关系数是0.99。使用模拟测试可以减少66.67%的分析工作时间，同时减少77.78%的仪器使用时间和介质体积。使用计算机模拟进行缓释制剂开发的实验设计，可以事半功倍。
结 论
使用DDDPlus和实验设计相结合的释放模拟可成功应用于多沙唑嗪缓释片剂的药物开发。并且，DDDPlus模拟可以通过减少需进行的实验数量来帮助公司节省时间和降低实验室成本。此外，处方设计的预测模型可用来对药物释放模型进行适宜的设计空间设置。
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请点击链接：https://mp.weixin.qq.com/s/8YxjOuFoaPS-C0ylAQUeFA
【案例揭秘】高溶解和低溶解药物（按BCS分类）的固有溶出模拟
来源：【研如玉公众号】
摘 要
固有溶出测试是对药物以特有的表面积在特定溶出介质中的溶解速率进行表征。这可用于确定药物是高溶解度还是低溶解度。本文中，使用DDDPlus 4.0版（Simulations Plus，Inc。）进行乙胺嘧啶和甲硝唑的固有溶出实验。乙胺嘧啶低溶解度，甲硝唑高溶解度。软件预测的固有溶出速率（IDR）与体外实测的固有溶出速率比较。将每个药物的物化参数(文献值)和固有溶出速率测试的试验条件作为软件的输入数据。该程序能够预测乙胺嘧啶和甲硝唑在pH值1.0~7.2的IDR。两种药物的实测IDR和软件预测的IDR显示出高相关性（R2＞0.9424）。模拟的IDR值显示乙胺嘧啶和甲硝唑在不同pH值介质中的溶解度，便于我们按BCS对其溶解度进行分类。使用DDDPlus进行固有溶出度模拟测试可用于获得某一药物的BCS溶解度分类，有助于减少实验室实验的数量。
讨 论
使用DDDPlus进行的计算机模拟可以帮助您在药物开发过程中获得对API的生物性能理解。可以使用软件模拟来预测API在生理相关pH值（pH1~7.2）的固有溶出。这可以帮助简化和最大程度地减少实验室实验工作。关键实验可以通过模拟识别，并通过实验结果加以确认，以表征API重要的生物性能。
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请点击链接：https://mp.weixin.qq.com/s/uXZvgHKpMFNkZwt_yz9myg
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导 读
大阳集团娱乐网址777技术部选取和汇总了2015-2020年制剂体外崩解与溶出模拟软件DDDPlus发表的应用文章，并将标题翻译成中文。
希望对您的业务或专业学习有所帮助，内容如下：
1. 采用基于生理的吸收模型，探讨食物和胃液pH值变化对恩曲替尼Entrectinib药代动力学的影响
Parrott N, Stillhart C, Lindenberg M, Wagner B, Kowalski K, Guerini E, Djebli N, Meneses-Lorente G. AAPS J (2020) 22:78. IF= 3.737
2. 使用基于生理的生物药剂学模型（PBBM）预测速释制剂空腹和餐后的生物等效性
Jereb R, Kristl A, Mitra A. Eur J Pharm Sci. Volume 155, 1 December 2020, 105554. IF= 3.616
3. 制剂处方开发早期的无定型固体分散剂：使用DDDPlus预测辅料对溶出曲线的影响
Amorphous Solid Dispersions in Early Stage of Formulation Development: Predicting Excipient Influence on Dissolution Profiles Using DDDPlus.
Njoku JO, Mukherjee D, Webster GK, Löbenberg R. Dissolut Technol.MAY 2020. IF= 0.674
4. 药物制剂早期开发的计算机预测工具：所需的数据和软件的功能
In silico Tools at Early Stage of Pharmaceutical Development: Data Needs and Software Capabilities.
Njoku JO, Amaral Silva D, Mukherjee D, Webster GK, Löbenberg R.AAPS PharmSciTech. (2019) 20: 243. IF=2.401
5. 采用生理药代动力学PBPK模型，评估影响美托洛尔缓释药品生物等效性的制剂因素
Physiologically Based Pharmacokinetic Modeling to Evaluate Formulation Factors Influencing Bioequivalence of Metoprolol Extended-Release Products.
Sumit Basu, Haitao Yang, Lanyan Fang, Mario Gonzalez‐Sales, Liang Zhao, Mirjam N. Trame, Lawrence Lesko, Stephan Schmid. J Clin Pharmacol. Volume59, Issue9. September 2019 Pages 1252-1263. IF=2.425
6. 评估制剂处方变异对临床的影响：以美托洛尔缓释制剂作为研究案例
Evaluating the Clinical Impact of Formulation Variability: A Metoprolol Extended‐Release Case Study.
Kim S, Sharma VD, Lingineni K, Farhan N, Fang L, Zhao L, Brown J, Cristofoletti R, Vozmediano V, Ait-Oudhia S, Lesko LJ, Trame MN, Schmidt S. J Clin Pharmacol. May 14, 2019. IF=2.425
7. 建立体外-体内相关的溶出和转化建模策略-某研讨会总结汇总
Dissolution and Translational Modeling Strategies Toward Establishing an In Vitro-In Vivo Link-a Workshop Summary Report.
Heimbach T, Suarez-Sharp S, Kakhi M, Holmstock N, Olivares-Morales A, Pepin X, Sjögren E, Tsakalozou E, Seo P, Li M, Zhang X, Lin HP, Mitra A, Morris D, Patel N, Kesisoglou F. AAPS J. Feb 11, 2019.IF=3.737
8. 用于体外-体内外推转化IVIVE的生理药代动力学PBPK建模：强调溶出数据的使用
Physiologically Based Pharmacokinetic (PBPK) Modelling for In Vitro-In Vivo Extrapolation: Emphasis on the Use of Dissolution Data
Vivek M. Ghate, Pinal Chaudhari, and Shaila A. Lewis. Dissolut Technol. August 2019. IF= 0.674
9. 药品开发中的溶出测试：研讨会总结报告
Dissolution Testing in Drug Product Development: Workshop Summary Report
Andreas Abend, David Curran, Jesse Kuiper, Xujin Lu, Hanlin Li, Andre Hermans, Pramod Kotwal, Dorys A Diaz, Michael J Cohen, Limin Zhang, Erika Stippler, German Drazer, Yiqing Lin, Kimberly Raines, Lawrence Yu, Carrie A Coutant, Haiyan Grady, Johannes Krämer, Sarah Pope-Miksinski, Sandra Suarez-Sharp. AAPS J. 2019 Jan 28;21(2):21. IF=3.737
10. 通过计算机模拟溶出曲线，开发多沙唑嗪缓释片
In Silico Simulation of Dissolution Profiles for Development of Extended-Release Doxazosin Tablets.
Duque MD, Issa MG, Silva DA, Barbosa EJ, Löbenberg R, Ferraz HG.Dissolut Technol. NOVEMBER 2018. IF= 0.674
11. 用于评估膳食对缓释制剂释放的影响的生理学考虑和体外策略
Physiological Considerations and In Vitro Strategies for Evaluating the Influence of Food on Drug Release from Extended-Release Formulations.
Koziolek M, Kostewicz ES, Vertzoni M. AAPS PharmSciTech. Aug 28, 2018. IF=2.401
12. 采用体外方法评估空腹状态下，药物在小肠中的沉淀-PEARRL综述
In vitro methods to assess drug precipitation in the fasted small intestine – a PEARRL review.
O’Dwyer PJ, Litou C, Box KJ, Dressman JB, Kostewicz ES, Kuentz M, Reppas C. J Pharm Pharmacol. 2018 Jun 28. IF=2.571
13. 溶出过程中，十二烷基硫酸钠载药不完全和分散颗粒剂的扩散层内的空腹状态下的模拟肠液胶束
Incomplete Loading of Sodium Lauryl Sulfate and Fasted State Simulated Intestinal Fluid Micelles Within the Diffusion Layers of Dispersed Drug Particles During Dissolution
Kendra Galipeau, Michael Socki, Adam Socia, Paul A. Harmon.Journal of Pharmaceutical Sciences. 107 (2018) 156-169. IF=2.997
14. 针对BCS溶解度分类的高溶解度和难溶性药物的固有溶出度模拟
Intrinsic dissolution simulation of highly and poorly soluble drugs for BCS solubility classification.
Duque MD, Issa MG, Silva DA, Kakuda BAS, Rodrigues LNC, Löbenberg R, Ferraz HG. Dissolution Technologies. 2017 Nov.IF=0.674
15. 使用体外和计算机模拟模型证明超出FDA标准范围的崩解测试方法的可行性
Justification of disintegration testing beyond current FDA criteria using in vitro and in silico models
Lukas Uebbing, Lukas Klumpp, Gregory K Webster, Raimar Löbenberg. Drug Design, Development and Therapy. April 2017.IF=3.216
16. 采用DDDPlus™模拟药物的体外溶出行为
Simulation of In Vitro Dissolution Behavior Using DDDPlus™
Almukainzi M, Okumu A, Wei H, Löbenberg R. AAPS PharmSciTech.Feb, 2015. IF=2.401
17. 体外-体内相关性IVIVC：通用概念，方法，在法规监管中的应用
In vitro–in vivo correlations: general concepts, methodologies and regulatory applications [J].
González-García I, Mangas-Sanjuán V, Merino-Sanjuán M, et al. Drug development and industrial pharmacy, 2015, 41(12): 1935-1947. IF=2.365
18. 生物相关性溶出度方法研究进展
缪慧，阮昊，陈悦，洪利娅. 《中国现代应用药学》. 2018,35(01). 综合影响因子：1.2
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